Instructions to use nyu-mll/roberta-base-10M-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nyu-mll/roberta-base-10M-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nyu-mll/roberta-base-10M-2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nyu-mll/roberta-base-10M-2") model = AutoModelForMaskedLM.from_pretrained("nyu-mll/roberta-base-10M-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b6ab5d064abb27b89a04e6a2335c9c9ae8cd0ac20704948a97e676571b0479c
- Size of remote file:
- 501 MB
- SHA256:
- 15217b3f021f2ea50c3a78233a20183dc4ab58a00056d522ef6eb886e58f59eb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.